A Maximum Power Point Tracking Method Based on Extension Neural Network for PV Systems

Chao, Kuei‐Hsiang; Li, Ching-Ju; Wang, Meng-Huei

doi:10.1007/978-3-642-01507-6_84

Cited by 19 publications

(13 citation statements)

References 7 publications

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“…Many authors have used neural networks to estimate and forecast the global solar radiation and the clearness index [15][16][17][18][19]. There are also some examples where this approach has been used as a tool to track the maximum power point of a PV generator; see, for instance, [20,21].…”

Section: Neural Network Applied To Photovoltaic Simulationmentioning

confidence: 99%

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Piliougine

Elizondo

Mora‐López

et al. 2014

Progress in Photovoltaics

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A neural network for modelling photovoltaic modules using angle of incidence and clearness index is proposed. Engineers require methods to estimate the output of a photovoltaic plant depending on meteorological conditions. Therefore, models for the grid inverter and the generator must be provided, and their outputs must be combined. The connection between both models is related to the maximum power point of the generator and how it is tracked by the inverter. That maximum power point under specific conditions of irradiance and module temperature is determined by the I-V curve of the module, which must be simulated under those conditions. Algebraic procedures were used to simulate the I-V curve. Recently, neural networks have been used for the same purpose. Previous methods only take into account the irradiance and the module temperature. The model proposed is based on neural networks, and it uses not only the irradiance and the module temperature but also the angle of incidence and the instantaneous clearness index as additional inputs. The normalised clearness replaces the standard clearness index because it allows the removal of the hourly trend found in this last index. This new model improves the results obtained with previous ones as it can distinguish amongst samples with the same solar irradiance and temperature values but with different angle of incidence and instantaneous clearness index. Results show that this model could be used to improve the accuracy of the tools used to forecast the output of photovoltaic plants.

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Section: Neural Network Applied To Photovoltaic Simulationmentioning

confidence: 99%

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Piliougine

Elizondo

Mora‐López

et al. 2014

Progress in Photovoltaics

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“…. In addition, there are also many examples where this approach has been used as a tool to track the maximum power point of a PV generator, see for instance .…”

Section: Neural Network Applied To Pv Simulationmentioning

confidence: 99%

“…Neural networks have been used to estimate and forecast the global solar radiation or the clearness index such as the works by Elizondo et al [21], Yona et al [22], and Mora-López et al [23]. In addition, there are also many examples where this approach has been used as a tool to track the maximum power point of a PV generator, see for instance [24,25].…”

Section: Neural Network Applied To Pv Simulationmentioning

confidence: 99%

Photovoltaic module simulation by neural networks using solar spectral distribution

Piliougine

Elizondo

Mora‐López

et al. 2012

Progress in Photovoltaics

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A novel methodology based on artificial neural networks is proposed as an alternative to algebraic and numerical procedures to determine the I‐V curve of a module under different conditions. Although there are methods that use neural networks for approximating the I‐V curve, this is the first time that the measurement of the spectrum is incorporated as an input. In addition, a suitable selection of the training samples used to build the model is fundamental in order to get an accurate approximation. This is why a training sample selection based on a Kohonen self‐organizing map is performed in this paper instead of a random selection. With the use of this preliminary step, the performance of the network trained with spectral information improves over the one without spectral information. Copyright © 2012 John Wiley & Sons, Ltd.

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“…where o N = 0 for i = 4, j = 4. The second part, E 2 = (1/2)B(ΔD k ref ) 2 is related to the output of FLC and depends only on neurons N i (i = 8,9,10,11,12,13,14,15,16). The ΔD k ref can be defined by defuzzification by using the centroid method and is written as:…”

Section: Integrating Hnn and Flcmentioning

confidence: 99%

“…A number of studies on MPPT have concentrated on the application of artificial neural network (ANN) [14,15]. In most of these ANN-based methods, large number of field data considering atmospheric conditions are required to train the ANN.…”

Section: Introductionmentioning

confidence: 99%

Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System

Subiyanto

Mohamed

Shareef

2012

International Journal of Photoenergy

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This paper presents a Hopfield neural network (HNN) optimized fuzzy logic controller (FLC) for maximum power point tracking in photovoltaic (PV) systems. In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. As in any fuzzy system, initial tuning parameters are extracted from expert knowledge using an improved model of a PV module under varying solar radiation, temperature, and load conditions. The linguistic variables for FLC are derived from, traditional perturbation and observation method. Simulation results showed that the proposed optimized FLC provides fast and accurate tracking of the PV maximum power point under varying operating conditions compared to that of the manually tuned FLC using trial and error.

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A Maximum Power Point Tracking Method Based on Extension Neural Network for PV Systems

Cited by 19 publications

References 7 publications

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Photovoltaic module simulation by neural networks using solar spectral distribution

Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System

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